MAINTAiN - Multi-scAle INTegrity assessment for Advanced high-temperature Nuclear systems

Lead Research Organisation: University of Oxford
Department Name: Materials

Abstract

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Publications

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Liu F (2020) A new method to model dislocation self-climb dominated by core diffusion in Journal of the Mechanics and Physics of Solids

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Liu F (2020) An improved method to model dislocation self-climb in Modelling and Simulation in Materials Science and Engineering

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Liu F (2023) Dislocation climb driven by lattice diffusion and core diffusion in Journal of the Mechanics and Physics of Solids

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Liu FX (2021) Dislocation dynamics modelling of the creep behaviour of particle-strengthened materials. in Proceedings. Mathematical, physical, and engineering sciences

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Haley J (2019) Helical dislocations: Observation of vacancy defect bias of screw dislocations in neutron irradiated Fe-9Cr in Acta Materialia

 
Description A new method to model dislocation self climb has been developed and implemented into 3D discrete dislocation dynamics code to allow simulation of creep and dislocation precipitate interactions.
Exploitation Route The methods and code developed are available online for anyone to use and could be used to simulate a range of problems.
Sectors Energy
 
Description Senior Research Fellowship
Amount £760,000 (GBP)
Organisation Royal Academy of Engineering 
Sector Charity/Non Profit
Country United Kingdom
Start 03/2022 
End 02/2027
 
Title 3D Discrete Dislocation Plasticity Code 
Description Finite domain discrete dislocation plasticity code to allow easier simulations of micro mechanical tests such as nano-indentation, microcatilever beam bending and pillar compression. Written mainly in Matlab and based on original DDLab from Lawrence Livermore National lab. 
Type Of Material Computer model/algorithm 
Year Produced 2021 
Provided To Others? Yes  
Impact Code provided new insights into the relationship between dislocation structure evolution and mechanical properties. Several papers have resulted from the development and use of this code. 
URL https://github.com/TarletonGroup/EasyDD